Latest Advances in Diagnosis and Treatment of Women-Associated Cancers

Herausgeber: Rashid, Shazia; Rashid, Sabia; Saxena, Ankur

• Gebundenes Buch

Produktbeschreibung

There has been tremendous progress in cancer diagnosis and treatment methodologies, and this book focusses on major cancers of cervix, breast, endometrium, and the associated reproductive system affecting women, using computational tools, Nano medicine, Machine Learning (ML), Artificial Intelligence (AI), Big Data and other latest techniques.

Produktdetails

• Verlag: Taylor & Francis Ltd
• Seitenzahl: 132
• Erscheinungstermin: 12. Juli 2022
• Englisch
• Abmessung: 216mm x 140mm x 11mm
• Gewicht: 318g
• ISBN-13: 9781032196367
• ISBN-10: 103219636X
• Artikelnr.: 64104709

Autorenporträt

Dr. Shazia Rashid is an academician, working as an Assistant Professor at the Amity Institute of Biotechnology (AIB) and Adjunct Faculty at the Amity Institute of Molecular Medicine and Stem Cells (AIMMSCR), Amity University Uttar Pradesh, Noida, India. Dr. Ankur Saxena is currently working as an Assistant Professor at Amity University Uttar Pradesh, Noida Dr. Sabia Rashid is currently working as a consultant hematologist at Queen Elizabeth Hospital and Kings College Hospital in London, UK

Inhaltsangabe

Preface. Acknowledgements. About the Editors. List of Contributors. List of
Abbreviations. Glossary. Overview of Traditional Methods of Diagnosis and
Treatment for Women-Associated Cancers. Cancer Drugs and Treatment
Formulations for Women-Associated Cancers. Imaging as an Important Tool for
Diagnosis of Breast Cancer. Use of Immunotherapy in Gynaecological and
Breast Cancer. Computational Drug Discovery and Development Along With
Their Applications in the Treatment of Women-Associated Cancers. Advances
in Nanotechnology for Treatment of Women-Specific Cancers. Identifying
Breast Cancer Treatment Biomarkers Using Transcriptomics. Integrating CADD
and Herbal Informatics Approach to Explore Potential Drug Candidates
Against HPV E Associated With Cervical Cancer. Advances in Big Data and
Machine Learning in Cancer Detection in Women-Associated Cancers.
Clinicians' Perspective in the Use and Adaptability of the Latest Methods
of Diagnosis and Treatment for Cancers in Women. Index.